Method for Automated High Throughput Identification of Carbohydrates and Carbohydrate Mixture Composition Patterns as well as Systems Therefore

ABSTRACT

The present invention relates to methods for the identification of compounds in carbohydrate mixture compositions as well as the determination of carbohydrate mixture composition patterns, based on e.g. orthogonal cross determining migration time (indices) using capillary gel electrophoresis-laser induced fluorescence and identifying said carbohydrate components based on comparing said migration time (indices) with standard migration time (indices) from a database which data are preferably also orthogonal cross determined. In a further aspect, the present invention relates to a method for carbohydrate mixture composition pattern profiling, like glycosylation pattern profiling using capillary gel electrophoresis-laser induced fluorescence (CGE-LIF). In another aspect, the present invention refers to a system for an automated determination and/or identification of carbohydrates and/or carbohydrate mixture composition patterns (e.g.: glycosylation patterns). Finally, the present invention relates to a database containing e.g. orthogonal cross normalized migration times and/or migration time indices of carbohydrates.

The present invention relates to methods for identification ofcarbohydrates compositions, e.g. out of complex carbohydrate mixtures,as well as the determination of carbohydrate mixture compositionpatterns (e.g.: of glycosylation patterns) based on e.g. orthogonalcross determining migration time indices using capillary gelelectrophoresis-laser induced fluorescence and identifying saidcarbohydrate components based on comparing said migration time indiceswith standard migration time indices from a database which data arepreferably also orthogonal cross determined. In a further aspect, thepresent invention relates to a method for carbohydrate mixturecomposition pattern profiling (e.g.: for glycosylation patternprofiling) using capillary gel electrophoresis-laser inducedfluorescence (CGE-LIF) generating electropherograms from said mixtures.In another aspect, the present invention refers to a system for anautomated determination and/or identification of carbohydrates and/orcarbohydrate mixture composition patterns, (e.g.: glycosylationpatterns). Finally, the present invention relates to a software packagefor data-processing and result-visualization, having an integrateddatabase containing e.g. orthogonal cross normalized migration times ofcarbohydrates.

BACKGROUND OF THE INVENTION

The importance of glycosylation in many biological processes is commonlyaccepted and has been discussed in detail throughout the literature overthe last 30 years. Glycosylation is a common and highly diversepost-translational modification of proteins in eukaryotic cells. Variouscellular processes have been described, involving carbohydrates on theprotein surface. The importance of glycans in protein stability, proteinfolding and protease resistance have been demonstrated in theliterature. In addition, the role of glycans in cellular signalling,regulation and developmental processes has been demonstrated in the art.

The oligosaccharides are mainly attached to the protein backbone, eitherby N- (via Asn) or O-(via Ser-Thr) glycosidic bonds, whereasN-glycosylation represents the more common type found in glycoproteins.Variations in glycosylation site occupancy (macroheterogeneity), as wellas variations in these complex sugar residues attached to oneglycosylation site (microheterogeneity) result in a set of differentprotein glycoforms. These have different physical and biochemicalproperties, which results in additional functional diversity. Inmanufacturing of therapeutic proteins in mammalian cell cultures, macro-and microheterogeneity were shown to affect properties like proteinsolubility, structural stability, protease resistance, or biological andclinical activity, see for example Butler, M., Cytotechnology, 2006, 50,57-76. For instance, the relevance of the glycosylation profile for thetherapeutic profile of monoclonal antibodies is well documented; seee.g. Parekh, et al., Nature, 1985, 316, 452-457.

Glycan biosynthesis is a non-template-driven process, involving the cellglycosylation machinery. N-glycan structures are also depending onvarious factors during the production process, like substrates levelsand other culture conditions. Thus, the glycoprotein manufacturing doesnot only depend on the glycosylation machinery of the host cell but alsoon external parameters, like cultivation conditions and theextracellular environment. Culture parameters affecting glycosylationinclude temperature, pH, aeration, supply of substrates or accumulationof byproducts such as ammonia and lactate. In case of recombinantglycoprotein or antibody manufacturing, characterization ofglycosylation profiles attracts increasing interest. In particular,because of regulatory reasons, the glycosylation profile of drugs has tobe determined.

Today, complex soluble but also oligomeric and/or polymeric carbohydratemixtures, obtained synthetically or from natural sources, like plants orhuman or animal milk are used as nutrition additives or inpharmaceuticals. The occurrence of sialic acids or sialic acidderivatives and the occurrence of monosaccharides having a phosphate,sulphate or carboxyl group within those complex natural carbohydrates iseven increasing their complexity. Because of this complexity, thoseprebiotic oligo- or polysaccharides, like neutral or acidicgalacto-oligosaccharides, long chain fructo-oligosaccharides, which canhave nutritional and/or biological effects, are gaining increasinginterest for food and pharmaceutic industry.

A wide range of strategies and analytical techniques for analysingglycoproteins, glycopeptides and released N-glycans or O-glycans havebeen established including e. g 2D-HPLC profiling, mass spectrometry andlectin affinity chromatography, as reviewed by Geyer and Geyer,Biochimica Et Biophysica Acta-Proteins and Proteomics 2006, 1764,1853-1869 and Domann et al., Practical Proteomics, 2007, 2, 70-76.

To obtain structural data of complex molecules, today carbohydrates areeither analysed by mass spectrometry (MS) or nuclear magnetic resonancespectroscopy (NMR) which are generally laborious and time consumingtechniques regarding sample preparation and data interpretation.

Each of these techniques has advantages as well as drawbacks. Choosingone, respectively a set of these methods for a given problem can becomea time- and labor-intensive task. For example, NMR provides detailedstructural information, but is a relatively insensitive method (nmol),which can not be used as a high-throughput method. Using MS is moresensitive (fmol) than NMR. However, quantification can be difficult andonly unspecific structural information can be obtained withoutaddressing linkages of monomeric sugar compounds. Both techniquesrequire extensive sample preparation and also fractionation of complexglycan mixtures before analysis to allow evaluation of the correspondingspectra. Furthermore, a staff of highly skilled scientists is requiredto ensure that these two techniques can be performed properly.

Although separation techniques based on the capillary electrophoresisprinciple, like capillary gel electrophoresis where considered forcomplex carbohydrate separation in the art before, e.g Callewaert, N. etal, Glycobiology 2001, 11, 275-281, WO 01/92890, Callewaert, N. et al,Nat Med. 2004, 10 429-434, there is still an ongoing need for a reliableand fast system allowing automated high throughput carbohydrateanalysis.

As identified by Domann et al, normal phase chromatography and capillarygel electrophoresis have an excellent selectivity for the analysis offluorescently labelled glycans. Serious drawbacks regarding the limit ofdetection and the linear dynamic range of CGE-LIF compared to normalphase liquid chromatography with fluorescence detection have beenreported. Furthermore with respect to CGE-LIF no methods are describedin the art allowing to rapidly monitor alterations in carbohydratemixture composition patterns (e.g. glycosylation patterns) includingfast and straightforward structure elucidation, without the need forcomplex data evaluation. Further, there is a need in the art to providemeans and methods allowing for determination and identification ofcarbohydrate mixtures of unknown composition enabling identification ofthe carbohydrate structures. In particular, there is a need for asensitive but reproducible and robust system and method allowing theidentification or determination of carbohydrate mixture compositionpatterns (e.g.: glycosylation patterns) as well as of carbohydratecompositions of unknown constitution in automated high throughput mode.In particular, for the latter, the method and system must ensure veryaccurate and reproducible analysis of carbohydrates whereby saidanalysis is essentially independent from sample type and origin,timepoint of analysis, laboratory, instrument and operator.

DESCRIPTION OF THE DRAWINGS

FIG. 1 provides a workflow of the carbohydrate analysis according to thepresent invention (right side) and of the prior art (left side).

FIG. 2 provides a fingerprint comparison of glycan pools of the membraneglycoprotein “hemagglutinin” of two different influenza virus strains.

FIG. 3 shows the orthogonal cross standardization of migration timesusing two orthogonal internal standards, double normalizing migrationtimes to internal standard indices. First standard: DNA base pairladder, sizes are given in the table, second standard: set ofcarbohydrates (arrows) located outside the range of presence ofcompounds to be analyzed.

FIG. 4 shows the standardization of the migration indices calculated as“bp-indices”.

FIG. 5 shows an analysis of an unknown carbohydrate composition obtainedfrom glycans together with the identification of carbohydrate componentsbased on the bp-index matching using the database according to thepresent invention.

FIG. 6 shows the polynomial-fit of internal standard peaks.

FIG. 7 provides an overlay of sample electropherograms from severalruns, adjusted to each other via polynomial-fit.

DETAILED DESCRIPTION OF THE PRESENT INVENTION

In a first aspect, the present invention relates to a method for anautomated determination and/or identification of carbohydrates and/or acarbohydrate mixture composition pattern profiling (e.g.: glycosylationpattern profiling) comprising the steps of:

a) obtaining a sample containing at least one carbohydrate;b) labelling said carbohydrate(s) with a fluorescent label;c) providing standard(s) of known composition;d) determining the migration time(s) of said carbohydrate(s) and of thestandard(s) of a known composition using capillary gelelectrophoresis-laser induced fluorescence;e) normalizing the migration time(s) of step d) to indices based ongiven standard migration time indice(s) of the standard(s);(f) comparing the migration time indice(s) of the carbohydrate(s) withstandard migration time indice(s) from a database;g) identifying or determining the carbohydrate(s) and/or thecarbohydrate mixture composition patterns.

In a preferred embodiment, the migration time(s) of step d) areorthogonal cross normalized using two different standards.

In the following the term “carbohydrate(s)” refers to monosaccharide(s),like glucose, galactose, mannose, fructose, fucose,N-acetylglucoseamine, sialic acid; disaccharide(s), like lactose,sucrose, maltose, cellobiose; oligosaccharide(s), like N-glycans,O-glycans, galactooligosaccharides, fructooligosaccharides; andpolysaccharide(s), like amylase, amylopektin, cellulose, glycogen,glycosaminoglycan, or chitin.

The term “glycoconjugate(s)” as used herein means compound(s) containinga carbohydrate moiety, examples for glycoconjugates are glycoproteins,glycopeptides, proteoglycans, peptidoglycans, glycolipids, GPI-anchors,lipopolysaccharides.

The term “carbohydrate mixture composition pattern profiling” as used inmeans establishing a pattern specific for the examined carbohydratemixture composition based on the number of different carbohydratespresent in the mixture, the relative amount of said carbohydratespresent in the mixture and the type of carbohydrate present in themixture and profiling said pattern e.g. in a diagram or in a graphic,e.g. as an electropherogram. Thus, fingerprints illustrated e.g. in formof an electropherogram, a graphic, or a diagram are obtained. Forexample glycosylation pattern profiling based on fingerprints fall intothe scope of said term. In this connection, the term “fingerprint” asused herein refers to electropherograms being specific for acarbohydrate or carbohydrate mixture, a diagram or a graphic.

The term “quantitative determination” or “quantitative analysis” refersto the relative and/or absolute quantification of the carbohydrates.Relative quantification can be done straight foreward via the individualpeak heights of each compound, which corresponds linear (within thelinear dynamic range of the LIF-detector) to its concentration. Therelative quantification outlines the ratio of each of one carbohydratecompound to another carbohydrate compound(s) present in the compositionor the standard. Further, absolute (semi-)quantitative analysis ispossible.

The term “orthogonal cross normalization” resp., “orthogonal crossstandardization” of migration times refer to double normalization ofmigration times by two sets of completely different (orthogonal)internal standards. Said orthogonal internal standdards of knowncomposition, e.g. can be a standard DNA base pair ladder (fluorescentlylabelled with a different tag than the carbohydrate samples) and a pairof carbohydrates (e.g. mono-, di-, tri-, tetra- and/or pentamer and a20mer or higher) fluorescently labelled with the same tag than thecarbohydrate samples, but eluting/mibrating out of range of thefingerprint of the carbohydrate samples to be analysed.

The present inventors found that using capillary gel electrophoresiswith laser induced fluorescence (CGE-LIF) allows a fast but robust andreliable analysis and identification of carbohydrates and/orcarbohydrate mixture composition patterns (e.g.: glycosylation patternsof glycoproteins). The methods according to the present invention usedin the context of glycoprotein analysis allow to visualizecarbohydrate-mixture compositions (e.g.: glycan-pools of glycoproteins)including structural analysis of the carbohydrates while omitting highlyexpensive and complex equipment, like mass spectrometers orNMR-instruments. Due to its superior separation performance andefficiency compared to other separation techniques, capillaryelectrophoresis techniques, in particular, capillary gel electrophoresisare considered for complex carbohydrate separation before but saidtechnique was not recommended in the art due to drawbacks which shouldallegedly provided when using said method, see e. g. Domann et al. orWO2006/114663. However, when applying the method according to thepresent invention, the technique of CGE-LIF allows for sensitive andreliable determination and identification of carbohydrate structures. Inparticular, the use of a capillary DNA-sequencer, (e. g. 4-CapillarySequencers: 3100-Avant Genetic Analyzer and 3130 Genetic Analyzer;16-Capillary Sequencer: 3100 Genetic Analyzer and 3130xl GeneticAnalyzer; 48-Capillary Sequencer: 3730 DNA Analyzer; 96-CapillarySequencer: 3730xl DNA Analyzer from Applied Biosystems) allows theperformance of the method according to the present invention. Theadvanced method of the invention enables the characterization ofvariations in complex composed natural or synthetic carbohydratemixtures and the characterization of carbohydrate mixture compositionpatterns (e.g.: protein glycosylation patterns), directly bycarbohydrate “fingerprint” alignment in case of comparing sampled withknown carbohydrate mixture compositions.

The method according to the present invention is a relatively simple androbust but nevertheless highly sensitive and reproducible analysismethod with high separation performance.

Especially the combination of the above mentioned instruments with up to96 capillaries in parallel and the software/database tool enclosedwithin the invention, enables an automated high throughput analysis.

In another aspect, the present invention relates to a method for anautomated carbohydrate mixture composition pattern profiling comprisingthe steps of:

a) providing a sample containing a carbohydrate mixture composition;b) labelling of said carbohydrate mixture composition with a fluorescentlabel;c) providing a second sample having a known carbohydrate mixturecomposition pattern to be compared with;d) generating electropherogramms (fingerprints) of the carbohydratemixture compositions of the first and second sample using capillary gelelectrophoresis-laser induced fluorescence;e) comparing the obtained electropherogramms (fingerprints) of the firstsample and the second sample;f) analyzing the identity and/or differences between theelectropherogramms (fingerprints) of the first and the second sample.

As shown in FIG. 1 outlining a workflow of carbohydrate analysis, afterprocessing of samples, electropherograms—“fingerprints”—are generatedand, according to the method of the present invention, simple andstraightforward structural investigation and identification ofcarbohydrates of a test sample (e.g. glycan pool of glycoproteins) viaautomatic matching their normalized CGE migration times (migration timeindices) with normalized CGE migration times of carbohydrates with knownstructures derived from a database whereby said normalized CGE migrationtimes are preferably orthogonal cross normalized CGE migration times, ispossible.

The database which is an essential element of the present invention,also referred to as “fingerprint library”, contains structuralinformation of known carbohydrates having assigned specific normalizedmigration time indices, namely CGE migration times. This database allowsautomated, fast and straightforward structural identification ofcarbohydrates from natural and recombinant glycoproteins afterprocessing or any other sample containing mono-, oligo-, orpolysaccharides by simple assignment of peaks from fingerprints tocarbohydrates with known structures. This can be done fully automatedvia migration time matching of test samples with e g. orthogonal crossnormalized migration times (migration time indices) of carbohydratesfrom corresponding database in a high throughput mode using a CGE-LIFsystem with e.g. up to 96 capillaries or more in parallel.

In a preferred embodiment, the test sample contains a mixture ofcarbohydrates. Preferably, said carbohydrates to be identified ordetermined are oligosaccharides. For example, saidoligosaccharides/glycans are obtained from processing of glycoproteins,as e g. shown in FIG. 1. That is, the sample represents an extraction ofglycans and the method according to the present invention allows for theidentification of a glycosylation pattern profile.

The invention is based on separating and detecting said carbohydratemixtures (e.g.: glycan pools) utilizing the CGE-LIF technique, e.g.using a capillary DNA-sequencer which enables generation of carbohydratecomposition pattern fingerprints, the automatic structure analysis ofthe separated carbohydrates via database matching of the preferablyorthogonal cross normalized CGE-migration time of each single compoundof the test sample mixture. The method claimed herein allowscarbohydrate mixture composition profiling of synthetic or naturalsources, like glycosylation pattern profiling of glycoproteins. Thenormalization of the migration times of the carbohydrates to migrationtime indices is based on the usage of a standard of known composition.In case of orthogonal cross normalization, two different standards ofknown composition and size are used. In particular, a first standard ofknown composition is preferably a standard base pair ladderconventionally used in a DNA-sequencer. The use of said standard basepair ladder allows to normalize each run of the DNA sequencer. Thus, anindividual bp-index for each of the carbohydrate molecules is obtained.In case of the preferred embodiment of orthogonal cross normalization,as a second orthogonal internal standard, a set of fluorescentlylabelled carbohydrates which elute/migrate out of range of thefingerprint of the samples to be analyzed. This set (two or more) offlourescently labelled carbohydrates, e.g. can be labelled carbohydratesmono-, di-, tri-, tetra-, and/or pentamer and a labelled carbohydrates20-mer (or higher). The monomer elutes in front of the sample moleculeswhile the oligomer(e.g. 20mer) is big enough to elute after the lastsample peat. This second internal standard can be used for the preciseadjustment of the calibration curve, regading y-axis intercept andslope.

This orthogonal cross determination of migration time indices allows anextremely exact and absolute reproducilbe CGE-LIF analysis ofcarbohydrates, independent form sample type and origin, timepoint ofanalysis, laboratory, instrument and operator.

The use of said method in combination with the system also allows toanalyze said carbohydrate mixture compositions quantitatively. Thus, themethod according to the present invention as well as the systemrepresents a powerful tool for monitoring variations in the carbohydratemixture composition like the glycosylation pattern of proteins withoutrequiring complex structural investigations. For fluorescently labelledcarbohydrates, the LIF-detection allows a limit of detection down to theattomolar range.

The standard necessary for normalization of each run may be present in aseparate sample or may be contained in the carbohydrate sample to beanalysed. Preferably, the standard(s) necessary are contained in eachsingle carbohydrate sample to be analysed.

The fluorescent label used for labelling the carbohydrates may be e.g.the fluorescent labels 8-amino-1,3,6-pyrenetrisulfonic acid alsoreferred to as 9-aminopyrene-1,4,6-trisulfonic acid (APTS),8-aminonaphtalene-1,3,6-trisulfonic acid (ANTS) or other preferablymultiple charged fluorescent dyes.

Based on the presence of the standard, preferably orthogonal standards,qualitative and quantitative analysis can be effected. Relativequantification can be done easily just via the individual peak heightsof each compound, which corresponds linear (within the linear dynamicrange of the LIF-detector) to its concentration.

The present invention resolves drawbacks of other methods known incarbohydrate analysis, like chromatography, mass spectrometry and NMR.NMR and mass spectrometry represent methods which are time and labourconsuming technologies.

In addition, expensive instruments are required to conduct said methods.Further, most of said methods are not able to be scaled up to hightroughput methods, like NMR techniques. Using mass spectrometry allows ahigh sensitivity. However, configuration can be difficult and onlyunspecific structural information could be obtained with addressinglinkages of monomeric sugar compounds. HPLC is also quite sensitivedepending on the detector and allows quantification as well. But asmentioned above, real high throughput analyses are only possible with anexpensive massive employment of HPLC-Systems and solvents.

Other techniques known in the art are based on enzymatic treatment whichcan be very sensitive and result in detailed structure information, butrequire a combination with other methods like HPLC, MS and NMR. Furthertechniques known in the art relates to lectin or monoclonal antibodyaffinity providing only preliminary data without given definitivestructural information.

The methods according to the present invention allow for high-throughputidentification of carbohydrates mixtures having unknown composition orfor high-throughput identification or profiling of carbohydrate mixturecomposition patterns (e,g.: glycosylation patterns of glycoproteins). Inparticular, the present invention allows determining the components ofthe carbohydrate mixture composition quantitatively.

The method of the present invention enables the fast and reliablemeasurement even of complex mixture compositions, and therefore enablesdetermining and/or identifying the carbohydrates and/or carbohydratemixture composition patterns (e.g.: glycosylation pattern) independentof the apparatus used but relates to the preferably orthogonal crossnormalized migration times (migration time indices) only.

The invention allows for application in diverse fields. For example, themethod maybe used for analysing the glycosylation of mammalian cellculture derived molecules, e.g. recombinant proteins, antibodies orvirus or virus components, e.g. influenza A virus glycoproteins.Information on glycosylation patterns of said compounds are ofparticular importance for food and pharmaceuticals. Starting with theseparation of complex protein mixtures by 1D/2D-gel-electrophoresis, themethod of the present invention could be used also for glycan analysisof any other glycoconjugates. Moreover, pre-purified glycoproteins, e.gby chromatography or affinity capturing, can be handled as well as bythe method according to the present invention, substituting the gelseparation and in-gel-degylcosylation step within-solution-deglycosylation, continuing after protein and enzymeprecipitation. Finally, complex soluble oligomeric and/or polymericsaccharide mixtures, obtain synthetically or from natural sources whichare nowadays important nutrition additives/surrogates or as used in oras pharmaceuticals can be analysed.

Thus, two types of analyses may be performed on the carbohydratemixtures. On the one hand, carbohydrate mixture composition patternprofiling like glycosylation pattern profiling may be performed and, onthe other hand, carbohydrate identification based on matchingcarbohydrate migration time indices with data from a database ispossible.

Therefore, a wide range of potential applications for the methodaccording to the present invention is given ranging form productionand/or quality control to early diagnosis of diseases which areproducing, are causing or are caused by changes in the glycosylationpatterns of glycoproteins.

In particular, in medical diagnosis, e.g. chronic inflammationrecognition or early cancer diagnostics, where changes in theglycosylation patterns of proteins are strong indicators for disease,the method may be applied. The variations in the glycosylation patterncould simply be identified by comparing the obtained fingerprintsregarding peak numbers, heights and migration times. Thus, diseasemarkers may be identified, as it is described in similar proteomicapproaches. It is, similar to comparing the proteomes of an individualat consecutive time points, the glycome of individuals could be analysedas indicator for disease or identification of risk patients.

Further, the method allows the differentiation of recombinant compoundsvis-a-vis with natural compounds. For example, said method may be usedin doping tests.

Another embodiment of the present invention relates to systems fordetermining and/or identifying carbohydrate mixture compositionpatterns, like glycosylation pattern profiling comprising a capillarygel electrophoresis-laser induced fluorescence apparatus; a dataprocessing unit comprising memory containing a database; and an outputunit.

Preferably, the capillary gel electrophoresis-laser induced fluorescenceapparatus is a capillary DNA sequencer. The database contains thenormalized migration times (migration time indices, e.g. given as basepair indices), prefrably, orthogonal cross normalized migration times.In the data processing unit raw data from the CGE-LIF-apparatus, likethe capillary DNA sequencer (sample trace and internal standard laddertrace) are extracted, e.g. extracted in ASCII-format and filed onvariables. The peaks within the electropherogram of the internalstandard of each sample are identified. For direct comparison ofdifferent runs, the internal standard peaks from the different runs areadjusted to each other via polynomial-fit, see also FIG. 6. The sampleelectropherograms are orhtogonal cross normalized to the adjusted timescale of its two orthogonal internal standards, each with its previouslydetermined individual polynomial-fit and precise readjustment of thecalibration curve, regarding the y-axis intercept and slope, and,therefore, they can be compared directly via simple overlay, see alsoFIG. 7, showing an overlay of sample electropherograms from several runsadjusted to each other via polynomial-fit.

This means, each measured sample electropherogram, which always runstogether with the internal standard, for example, the internal standardbase pair ladder, is translated/formatted by an algorithm from migrationtime to internal standard index and therefore normalized and indexed.Preferably, the standard is an orthogonal internal standards of theinternal base pair ladder and a pair of known carbohydrates layingoutside in size of the range of the carbohydrates to be analyzed (e.g.one monomer and one large oligomer) whereby said standard carbohydratesare preferably labelled with the same label as the carbohydrates to beanalyzed, thus, allowing orthogonal cross normalization. The processedand normalized data enable direct comparison of different runs and allowthe identification or exclusion of carbohydrates structures by simplemigration time index matching of carbohydrates peaks from the samplewith those already deposited within the data base. The invention enablesthe qualitative and quantitative analysis and the direct comparison oftest carbohydrates samples, independent from the operator, instrument orlaboratory, in particular, in case of orthogonal cross normalization.

Thus, another aspect of the present invention relates to the data basecontaining normalized migration times (indices) of carbohydrates, saidmigration time indices are based on CGE-LIF-measurement and are obtainedby

-   -   a) analysing known carbohydrate structures in a sample together        with an internal standard at the same time with a capillary gel        electrophoresis-laser induced fluorescence means;    -   b) determining the migration time of said carbohydrate        compound(s) and the standard;    -   c) normalizing the migration times of step b) based on given        standard migration time(s) of the internal standard;    -   d) assigning the normalized carbohydrate compound migration        times to standard migration time indices.

Preferably, the above database contains orthogonal cross normalizedmigration times using at least two different standards.

The home built data base Is integrated e.g. into the home built softwaretool and can be fitted by the user himself. Said data base may beprovided as an external tool or may be provided from a centralizedserver.

The structure data base/library with the assigned normalizedCGE-migration times, e.g. base pairs indices, is generated by measuringsingle carbohydrates of known structure. The general library/data basewhere the known carbohydrate structures are deposited together withtheir migration time indices, enable sample and system independent database matching of carbohydrate mixture composition patterns(fingerprints) like glycan pools from test samples. Preferably, saidnormalized CGE-migration times are orthogonal cross normalizedCGE-migration times.

The data from the CGE-LIF are extracted in computer readable form andfiled on variables. The peaks within the electropherogram of theinternal standard(s) of each sample are identified and annotated. Thetime scales of the sample electropherograms are normalized andtransferred from time to the internal standard domain, e.g. the basepair annotation, each to its corresponding internal standard trace, eachwith its previously determined individual polynomial-fit and in case oforthogonal standards and precies readjustment, and, therefore, thecompound peaks are normalized, preferably orthogonal cross normalized.

The data from the data base of carbohydrate standards of knownstructures allow to identify unknown compounds with identical,preferably orthogonal cross, normalized migration times, just viamatching their migration time indices with those from the library.

1. A method for an automated determination and/or identification ofcarbohydrates and/or carbohydrate mixture composition pattern profilingcomprising the steps of: a) obtaining a sample containing at least onecarbohydrate; b) labelling said carbohydrate(s) with a fluorescentlabel; c) providing a standard of known composition; d) determining themigration time(s) of said carbohydrate(s) and the standard of a knowncomposition using capillary gel electrophoresis-laser inducedfluorescence; e) normalizing the migration time(s) to migration timeindice(s) based on given standard migration time indice(s) of thestandard; f) comparing these migration time indice(s) of thecarbohydrate(s) with standard migration time indice(s) from a database;g) identifying or determining the carbohydrates and/or the carbohydratemixture composition patterns.
 2. A method for an automated carbohydratemixture composition pattern profiling comprising the steps of a)providing a sample containing a carbohydrate mixture composition; b)labelling of said carbohydrate mixture composition with a fluorescentlabel; c) providing a second sample having a known carbohydrate mixturecomposition pattern to be compared with d) generating electropherogramsof the carbohydrate mixture composition of the first and second sampleusing capillary gel electrophoresis-laser induced fluorescence; e)comparing the obtained electropherogram or the migration timescalculated therefrom of the first sample and the second sample; f)analysing the identity and/or differences between the carbohydratemixture composition pattern profiles of the first and the second sample.3. The method according to claim 1 whereby at least two orthogonalstandards are provided in step c) and orthogonal cross normalization isperformed in step e) pased on the given standard migration time indicesof the orthogonal standards.
 4. The method according to claim 1, whereinthe sample contains a mixture of carbohydrates.
 5. The method accordingto claim 1, wherein the sample is an extraction of glycans and themethod allows for the identification of a glycosylation pattern profile.6. The method according to claim 5, wherein the glycosylation pattern ofa glycoprotein is identified.
 7. The method according to claim 1,wherein the standard is a known carbohydrate mixture composition.
 8. Themethod according to claim 1, wherein the standard of known compositionis a standard base pair ladder.
 9. The method according to claim 3wherein the orthogonal standards of known composition are a standardbase pair ladder and a set of mono- and oligosacchardies.
 10. The methodaccording to claim 1, wherein the standard composition is added to thesample containing the unknown carbohydrate mixture composition.
 11. Themethod according to claim 1, wherein the fluorescent label is8-amino-1,3,6-pyrenetrisulfonic acid.
 12. The method according to claim1, wherein the method allow for high throughput identification ofcarbohydrate mixtures having unknown compositions.
 13. The methodaccording to claim 1, wherein the carbohydrate or the components of thecarbohydrate mixture is determined quantitatively.
 14. The methodaccording to claim 2, wherein orthogonal cross determined standardmigration time indices of the carbohydrates present in the sample arecalculated based on orthogonal internal standards of known composition.15. Database containing normalized migration times and or normalizedmigration time indices of carbohydrates, said migration times and/ormigration time indices are obtained by a) analysing known carbohydratestructure(s) in a sample together with an internal standard at the sametime with a capillary gel electrophoresis-laser induced fluorescentsmeans; b) determining the migration time(s) of said carbohydratecompound(s) and the standard; c) normalizing the migration time(s) ofstep b.) based on given migration time(s) of the internal standard; d)assigning the normalized carbohydrate compound(s) migration time(s) tostandard migration time indices.
 16. The database according to claim 15whereby the normalized migration times a orthogonal cross normalizedmigration times and indices and orthogonal internal standards are used.17. System for determining and/or identifying carbohydrate mixturecompositions and/or carbohydrate mixture composition pattern profilingcomprising a capillary gel electrophoresis-laser induced fluorescenceapparatus; a data processing unit comprising memory containing adatabase according to claim 15; and an output unit.
 18. A systemaccording to claim 17, wherein the capillary gel electrophoresis-laserinduced fluorescence apparatus is a capillary DNA-sequencer.